With the rapid development of machine learning techniques, various computational prediction approaches have been proposed to predict drug-target interactions.
[1]} integrated the relationship among the pharmacological space, the chemical space, and the topology of drug–target interaction networks to predict the associations between drugs and targets, and their experimental results have demonstrated that drug–target interactions are more correlated with pharmacological effect similarity than with chemical structure similarity.
According to the similarity of chemical information, [2]} proposed a method to explore the associations between drugs and targets. They selected 30 of predicted results for biological experiments and finally confirmed 23 with interrelationships.
[3]} used supervised machine learning methods to predict the relationship between drugs and targets. To solve the problem of sample imbalance, they are collecting the positive samples from the database, and the negative samples using the random selection method. The input features of the classifier consist of the chemical structure of the drug and the sequence information of the protein.
[4]} developed a novel method of Network-based Random Walk with Restart on the Heterogeneous (NRWRH) network to predict potential drug–target interactions on a large scale. The excellent experimental results show that the proposed method can discover new potential drug–target interactions for drug development.
These approaches provide feasible solutions to the problem. However, the extracted features used in these approaches only have limited coverage on interaction information, since they are dedicated to specific facets of the protein and the drug profiles.
